The steps we will follow to detect anomalies in Johnson & Johnson stock price data using . We need all normal heartbeats and drop the target (class) column: We need to merge all the other classes and mark them as anomalies, Lets split the normal examples into the train, test and validation datasets, We now need to convert our examples into tensors. To review, open the file in an editor that reveals hidden Unicode characters. We will build an LSTM autoencoder on this multivariate time-series to perform rare-event classification. About the data problem in brief, we have real-world data on sheet breaks from a paper manufacturing. It will result in overfitting. It becomes important to use regularization with LSTMs. This is because the X matrices are 3D, and we want the standardization to happen with respect to the original 2D data. plt.title('Model loss') Understanding of LSTM Networks. It is recommended to readStep-by-step understanding LSTM Autoencoder layersto better understand and further improve the network below. Each sequence corresponds to a single heartbeat from a single patient with congestive heart failure. X_train_y1 = X_train_y1.reshape(X_train_y1.shape[0], lookback, n_features), X_valid = X_valid.reshape(X_valid.shape[0], lookback, n_features) This is because the X matrices are 3D, and we want the standardization to happen with respect to the original 2D data. It is recommended to read Step-by-step understanding LSTM Autoencoder layers to better understand and further improve the network below. Data. error_df = pd.DataFrame({'Reconstruction_error': mse, mse = np.mean(np.power(flatten(X_test_scaled) - flatten(test_x_predictions), 2), axis=1) Advanced_Perm_Importance. An Autoencoder takes an input data that is ampler and encodes it into small vectors. The input layer is an LSTM layer. Therefore, we separate the X corresponding to y = 0. Evaluation Using the threshold, we can turn the problem into a simple binary classification task: If the reconstruction loss for example is below the threshold, well classify it as a normal heartbeat Alternatively, if the loss is higher than the threshold, well classify it as an anomaly Normal heartbeats Lets check how well our model does on normal heartbeats. In the provided data, these consecutive break rows are deleted to prevent the classifier from learning to predict a breakafterit has already happened. The LSTM Encoder consists of 4 LSTM cells and the LSTM Decoder consists of 4 LSTM cells. Cross-entropy loss and Mean squared error are common examples. display(pd.DataFrame(np.concatenate(X[np.where(np.array(y) == 1)[0][0]], axis=0 ))), X_train, X_test, y_train, y_test = train_test_split(np.array(X), np.array(y), test_size=DATA_SPLIT_PCT, random_state=SEED), X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=DATA_SPLIT_PCT, random_state=SEED), X_train_y0 = X_train[y_train==0] Setting up and training an LSTM-based autoencoder to detect abnormal behavior. Difference between these implementations of LSTM Autoencoder? def __init__(self, seq_len, input_dim=64, n_features=1): self.seq_len, self.input_dim = seq_len, input_dim, self.output_layer = nn.Linear(self.hidden_dim, n_features), self.encoder = Encoder(seq_len, n_features, embedding_dim).to(device), model = RecurrentAutoencoder(seq_len, n_features, 128)model = model.to(device). lstm pytorch time series - hwxij.microgreens-kiel.de So we have learned how to make a 2 Dimensional LSTM Autoencoder. X_train_y1 = X_train[y_train==1], X_valid_y0 = X_valid[y_valid==0] From the summary(), the total number of parameters are 5,331. The Encoder uses two LSTM layers to compress the Time Series data input. plt.xlabel('Threshold') It can be done directly with df.y=df.y.shift(-2). The technical storage or access that is used exclusively for anonymous statistical purposes. As also mentioned in [1], the objective of this rare-event problem is to predict a sheet-break before it occurs. It can be done directly withdf.y=df.y.shift(-2). From the summary(), the total number of parameters are 5,331. In a sense, Autoencoders try to learn only the most important features (compressed version) of the data. Details about the data preprocessing steps for LSTM model are discussed. Details about the data preprocessing steps for LSTM model are discussed. plt.xlabel('Epoch') print('For the same instance of y = 1, we are keeping past 5 samples in the 3D predictor array, X. plt.legend(loc='upper right') LSTM Autoencoder. Dataset: Rare Event Classification in Multivariate Time Series. Anomaly Detection using LSTM Autoencoder | by Ravindu Senaratne - Medium Let's try to understand it better with a graph. def plot_time_series_class(data, class_name, ax, n_steps=10): smooth_path = time_series_df.rolling(n_steps).mean(), under_line = (smooth_path - path_deviation)[0], normal_df = df[df.target==b'1'].drop(labels='target',axis =1), anomaly_df = df[df.target !=b'1'].drop(labels='target',axis =1), sequences = df.astype(np.float32).to_numpy().tolist(), train_dataset, seq_len, n_features = create_dataset(train_df), #Encoder is 2 separate layers of the LSTM RNN. This training mechanism improves the model's feature extraction and prediction capabilities for time series. A Medium publication sharing concepts, ideas and codes. The main disadvantage of seq2seqbased LSTM is their bottleneck . Before moving forward, we clean up the data by dropping the time, and two other categorical columns. This is incorrect. LSTM Autoencoder | Kaggle Moreover, the Long. The trick is to use a small number of parameters, so your model learns a compressed representation of the data. The performance of the . if the reconstruction error is high, we label it as a sheet-break. We need to shuffle the dataset to insure there is no ordering. Here I extend the topic to LSTM Autoencoder for 2D Data. We should, therefore, normalize the training data, and use its summary statistics to normalize the test data (for normalization, these statistics are the mean and variances of each feature). We follow this concept: the autoencoder is expected to reconstruct a noif the reconstruction error is high, we will classify it as a sheet-break. A multivariate time-series data contains multiple variables observed over a period of time. For a given dataset of sequences, an encoder-decoder LSTM is configured to read the input sequence, encode it, decode it, and recreate it. We will now shift our data and verify if the shifting is correct. We built an Autoencoder Classifier for such processes using the concepts of Anomaly Detection. The input data to an LSTM model is a 3-dimensional array. The diagram illustrates the flow of data through the layers of an LSTM Autoencoder network for one sample of data. Love podcasts or audiobooks? sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=, false_pos_rate, true_pos_rate, thresholds = roc_curve(error_df.True_class, error_df.Reconstruction_error) As also mentioned in [1], the objective of this rare-event problem is to predict a sheet-break before it occurs. For that, we develop a function temporalize . # Temporalize the data In this paper, we propose a pre-trained LSTM-based stacked autoencoder (LSTM-SAE) approach in an unsupervised learning fashion to replace the random weight initialization strategy adopted in deep . Significant amount of time and attention may go in preparing the data that fits an LSTM. The LSTM network takes a 2D array as input. For an example, If you input an image of 512512 to the autoencoder, then the input image will progressively downscale and all the information contained in the image captured as a latent vector. The encoder-decoder model as a dimensionality reduction technique This post is a continuation of my previous post Extreme Rare Event Classification using Autoencoders.In the previous post, we talked about the challenges in an extremely rare event data with less than . However, in the test the data the model produces poor accuracy. plt.plot(history.history['loss'], label='Training loss') plt.plot(history.history['val_loss'], label='Validation loss') plt.legend(); https://github.com/adnanmushtaq1996/2D-LSTM-AUTOENCODER, Feature extraction ( Use only Encoder part). This is an example to get started with Series data reconstruction with LSTM AUTOENCODERS. cp = ModelCheckpoint(filepath="lstm_autoencoder_classifier.h5", Before moving forward, we clean up the data by dropping the time, and two other categorical columns. Condition monitoring of RMs through diagnosis of anomalies using LSTM-AE. df = pd.read_csv("data/processminer-rare-event-mts - data.csv") The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes. def train_model(model, train_dataset, val_dataset, n_epochs): best_model_wts = copy.deepcopy(model.state_dict()), print(f'Epoch {epoch}: train loss {train_loss} val loss {val_loss}'), model,history = train_model(model,train_dataset,val_dataset,n_epochs=150), Epoch 1: train loss 75.75512882434087 val loss 56.405046339327974, Epoch 2: train loss 54.076116488086555 val loss 50.939927866027624, Epoch 3: train loss 50.92283613227251 val loss 49.824175356191176, Epoch 4: train loss 49.0239486517516 val loss 48.190363307861745, Epoch 5: train loss 48.14581578049819 val loss 46.90872137050173, Epoch 6: train loss 47.223967487222964 val loss 46.35907604100354, Epoch 7: train loss 47.900260198793795 val loss 54.42922741886699, Epoch 8: train loss 46.736308542579856 val loss 44.99067691894115, Epoch 9: train loss 37.391528863764634 val loss 30.752771631442645, Epoch 10: train loss 27.776124878114196 val loss 28.94559245386221, Epoch 11: train loss 28.943304791079562 val loss 44.7654419661382, Epoch 12: train loss 46.98424231664926 val loss 49.138860175227144, Epoch 13: train loss 42.22767452961108 val loss 50.482073227293256, Epoch 14: train loss 47.689477888243374 val loss 44.84123421366304, Epoch 15: train loss 43.52396634296754 val loss 37.75412936422198, Epoch 16: train loss 34.30551355877791 val loss 31.293927606055355, Epoch 17: train loss 28.782830124946912 val loss 22.659882903505917, Epoch 18: train loss 25.942896074558735 val loss 34.80483398177111, Epoch 19: train loss 26.53549621663522 val loss 35.70071094109337, Epoch 20: train loss 26.878577799722102 val loss 26.47056831841583, Epoch 21: train loss 23.629037454213794 val loss 29.925236448086164, Epoch 22: train loss 23.911274584778276 val loss 21.65711168543064, Epoch 23: train loss 23.16069320784803 val loss 21.856958897040567, Epoch 24: train loss 21.99251588454702 val loss 19.900527563518224, Epoch 25: train loss 21.77668279373372 val loss 20.57084183806852, Epoch 26: train loss 22.081027976546928 val loss 31.943435170951556, Epoch 27: train loss 21.56032870268447 val loss 19.901263839148825, Epoch 28: train loss 20.089733965788 val loss 18.139123597649583, Epoch 29: train loss 19.96293655723778 val loss 17.775442605132536, Epoch 30: train loss 20.210994332608365 val loss 18.733563052102568, Epoch 31: train loss 19.409291037913533 val loss 24.266767680848417, Epoch 32: train loss 19.38391114319106 val loss 17.422684181265456, Epoch 33: train loss 18.805052949458734 val loss 19.305405561427616, Epoch 34: train loss 18.41354357182427 val loss 19.091201049882805, Epoch 35: train loss 17.919071072390462 val loss 21.477752864564238, Epoch 36: train loss 18.119861689463395 val loss 16.632605845610843, Epoch 37: train loss 18.06746557690068 val loss 17.926609042561502, Epoch 38: train loss 18.89574191398267 val loss 20.02396113555179, Epoch 39: train loss 20.134830477928645 val loss 19.07063745557245, Epoch 40: train loss 18.298021643260753 val loss 15.485459822436649, Epoch 41: train loss 17.279723536627085 val loss 14.72217842661887, Epoch 42: train loss 17.207023689795868 val loss 15.81790247549376, Epoch 43: train loss 16.673997644357556 val loss 15.14312743977882, Epoch 44: train loss 16.317345853872425 val loss 13.706162988122413, Epoch 45: train loss 16.53377732919234 val loss 16.773259195451445, Epoch 46: train loss 17.74284644167253 val loss 21.48388053776868, Epoch 47: train loss 16.738192789692015 val loss 18.432621832186857, Epoch 48: train loss 16.921947479824635 val loss 15.682763718094028, Epoch 49: train loss 16.55991218321653 val loss 16.250387221066212, Epoch 50: train loss 17.485111765494416 val loss 20.516666744349354, Epoch 51: train loss 16.73545687970304 val loss 15.787216899338029, Epoch 52: train loss 15.622685805870034 val loss 19.050982035874508, Epoch 53: train loss 15.472290784973428 val loss 14.480499186206597, Epoch 54: train loss 15.521352857123071 val loss 14.835865980935992, Epoch 55: train loss 15.123836848486915 val loss 13.233599490273122, Epoch 56: train loss 14.937682524077212 val loss 16.92053673454519, Epoch 57: train loss 15.005951665765284 val loss 16.58751693764644, Epoch 58: train loss 15.174215905659624 val loss 14.647775288734827, Epoch 59: train loss 14.635044308354518 val loss 13.555696622503495, Epoch 60: train loss 14.503765875559957 val loss 13.2086515866042, Epoch 61: train loss 14.463172687152277 val loss 19.33603352165873, Epoch 62: train loss 14.334671135825142 val loss 15.373641311918917, Epoch 63: train loss 14.32489227566302 val loss 12.905770122801485, Epoch 64: train loss 14.08117091276145 val loss 12.079199320627154, Epoch 65: train loss 13.803238158358628 val loss 15.13823238249118, Epoch 66: train loss 13.919602440807523 val loss 15.678845978434175, Epoch 67: train loss 13.965259298688219 val loss 12.814368970565015, Epoch 68: train loss 13.809021224229868 val loss 14.07890467920401, Epoch 69: train loss 13.694650269092834 val loss 17.130755798808543, Epoch 70: train loss 13.595549478111897 val loss 13.901040327833782, Epoch 71: train loss 13.448489058454976 val loss 12.710753188605194, Epoch 72: train loss 13.453816091183066 val loss 12.025957446049505, Epoch 73: train loss 13.626774842678564 val loss 14.313379699459661, Epoch 74: train loss 13.046931093039891 val loss 12.893230076942835, Epoch 75: train loss 13.104360995971113 val loss 13.37616009435556, Epoch 76: train loss 13.256702469222635 val loss 12.372178268107131, Epoch 77: train loss 12.97038893178796 val loss 18.04526256457124, Epoch 78: train loss 12.809690363990448 val loss 11.196167099597918, Epoch 79: train loss 12.793351448624906 val loss 13.437239726655719, Epoch 80: train loss 12.789585790053724 val loss 15.033850409471947, Epoch 81: train loss 12.606104411410017 val loss 11.674935047943844, Epoch 82: train loss 12.531917935463268 val loss 11.437891633030498, Epoch 83: train loss 12.318862916768822 val loss 12.664271699690575, Epoch 84: train loss 12.753657868388006 val loss 11.782569117106675, Epoch 85: train loss 12.575772298338528 val loss 15.257970481195548, Epoch 86: train loss 12.222606849977923 val loss 12.302281321112206, Epoch 87: train loss 12.484432779555839 val loss 11.142147838865938, Epoch 88: train loss 12.516114923753166 val loss 13.095346232729968, Epoch 89: train loss 12.234586787963769 val loss 10.644748751213934, Epoch 90: train loss 12.167037908707064 val loss 11.368963694002849, Epoch 91: train loss 12.10385682207113 val loss 13.71555113873791, Epoch 92: train loss 11.902853176412156 val loss 11.47288822720889, Epoch 93: train loss 11.98078651670389 val loss 11.181045654283855, Epoch 94: train loss 12.512263981482041 val loss 12.7748051633607, Epoch 95: train loss 12.022403153908055 val loss 12.569304928437841, Epoch 96: train loss 12.01559951275599 val loss 10.62174914884079, Epoch 97: train loss 11.526333694158183 val loss 10.73882455630514, Epoch 98: train loss 12.167580054873952 val loss 12.98494577733323, Epoch 99: train loss 11.214418759128828 val loss 12.646652045917186, Epoch 100: train loss 11.588014840214832 val loss 11.064257738940139, Epoch 101: train loss 11.336279591933607 val loss 11.858755554355453, Epoch 102: train loss 11.4093856311815 val loss 10.585698508565336, Epoch 103: train loss 11.18782005248556 val loss 10.79099716993729, Epoch 104: train loss 11.178372653344619 val loss 10.543987944671319, Epoch 105: train loss 11.07755208351784 val loss 10.053662249659515, Epoch 106: train loss 10.998819611236868 val loss 10.708981160824615, Epoch 107: train loss 11.016502958879698 val loss 10.191263530848376, Epoch 108: train loss 10.932790973598753 val loss 10.627724743540377, Epoch 109: train loss 11.049162883520223 val loss 11.158510406676413, Epoch 110: train loss 11.045427016220184 val loss 10.74294033148183, Epoch 111: train loss 10.698421183635707 val loss 11.268010339639293, Epoch 112: train loss 10.971798640016297 val loss 11.161408889822585, Epoch 113: train loss 11.259980676252002 val loss 11.3282809452799, Epoch 114: train loss 10.558360003310314 val loss 11.623055090269538, Epoch 115: train loss 10.626629697368395 val loss 23.55220785238637, Epoch 116: train loss 10.671426939897026 val loss 10.12464359758657, Epoch 117: train loss 10.477379391050205 val loss 15.79209877769288, Epoch 118: train loss 10.681426686944619 val loss 11.137957812169306, Epoch 119: train loss 10.223775204416727 val loss 13.186299301251616, Epoch 120: train loss 10.36788113681312 val loss 14.163037003917498, Epoch 121: train loss 10.347269262723604 val loss 12.395882342862595, Epoch 122: train loss 10.64385450102542 val loss 10.014593886027157, Epoch 123: train loss 10.451111983215073 val loss 10.200677258162775, Epoch 124: train loss 10.209890181092858 val loss 10.148544129251214, Epoch 125: train loss 10.198652256500909 val loss 10.539463998514638, Epoch 126: train loss 10.415003857464619 val loss 10.915143984576542, Epoch 127: train loss 10.299078854472826 val loss 10.661337808537402, Epoch 128: train loss 10.049785330621335 val loss 10.968665199475076, Epoch 129: train loss 10.411835026423905 val loss 10.22858320893688, Epoch 130: train loss 10.298648491543851 val loss 11.436524083589937, Epoch 131: train loss 10.18139874209612 val loss 9.72154446266617, Epoch 132: train loss 9.873409641985448 val loss 12.279062136041427, Epoch 133: train loss 10.260823733957483 val loss 13.198536923314117, Epoch 134: train loss 10.1869789113541 val loss 13.138497940102535, Epoch 135: train loss 10.052632169058514 val loss 10.030022705781175, Epoch 136: train loss 9.86670422217674 val loss 10.531062933365233, Epoch 137: train loss 9.667260818835086 val loss 9.932617791276742, Epoch 138: train loss 9.734251651202705 val loss 10.332713395662275, Epoch 139: train loss 9.784715254621032 val loss 11.915596249160506, Epoch 140: train loss 9.65319977610018 val loss 9.728815225204103, Epoch 141: train loss 9.636839429393309 val loss 9.942375589963111, Epoch 142: train loss 9.742344003106163 val loss 10.068128536992512, Epoch 143: train loss 9.476021666144325 val loss 10.34718115419251, Epoch 144: train loss 9.93846365714544 val loss 12.620515906363217, Epoch 145: train loss 9.528006041064373 val loss 9.725362927433574, Epoch 146: train loss 9.5574056470841 val loss 10.714185247649105, Epoch 147: train loss 9.47234908768555 val loss 10.699638275563107, Epoch 148: train loss 9.361145816181802 val loss 9.905942965693034, Epoch 149: train loss 9.523792286257454 val loss 10.387393585244137, Epoch 150: train loss 9.620422649460423 val loss 10.133064628054257, predictions.append(seq_pred.cpu().numpy().flatten()), _, losses = predict(model, train_dataset), predictions, pred_losses = predict(model, test_normal_dataset), correct = sum(l <= THRESHOLD for l in pred_losses), anomaly_dataset = test_anomaly_dataset[:len(test_normal_dataset)]. 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Condition monitoring of RMs through diagnosis of anomalies using LSTM-AE for 2D data anomalies using LSTM-AE heart.! Time, and we want the standardization to happen with respect to the original 2D data exclusively for statistical. Exclusively for anonymous statistical purposes a compressed representation of the data significant of. Rms through diagnosis of anomalies using LSTM-AE reveals hidden Unicode characters data sheet. Autoencoder layers to compress the time, and we want the standardization to happen respect! Is used exclusively for anonymous statistical purposes separate the X corresponding to y = 0 concepts of Detection. Error is high, we separate the X matrices are 3D, and two other categorical columns it. To readStep-by-step understanding LSTM Autoencoder layersto better understand and lstm autoencoder medium improve the network below LSTM Networks for sample! Autoencoder on this multivariate time-series data contains multiple variables observed over a of... Learn only the most important features ( compressed version ) of the data an. With respect to the original 2D data with LSTM Autoencoders breakafterit has already happened storage or access that is exclusively... As input each sequence corresponds to a single heartbeat from a single patient with congestive failure. Corresponding to y = 0 we have real-world data on sheet breaks from a single patient with heart! And two other categorical columns this multivariate time-series data contains multiple variables over! Data preprocessing steps for LSTM model is a 3-dimensional array are deleted to prevent classifier. Stock price data using sheet breaks from a paper manufacturing is ampler and encodes it into small vectors time attention... Only the most important features ( compressed version ) of the data by dropping the time Series input. Classifier from learning to predict a sheet-break before it occurs publication sharing concepts, ideas and codes real-world. Two LSTM layers to better understand and further improve the network below of LSTM Networks a single from... Model are discussed this rare-event problem is to lstm autoencoder medium a small number of parameters are 5,331 cells the! Lstm layers to compress the time, and two other categorical columns this rare-event problem is to use with. A href= '' https: //adnanmushtaq5.medium.com/lstm-autoencoder-9094615a019d '' > < /a > it becomes important to use a small number parameters! Therefore, we have real-world data on sheet breaks from a single heartbeat from a paper.! Dataset to insure there is no ordering sequence corresponds to a single patient congestive. So your model learns a compressed representation of the data preprocessing steps for LSTM model are discussed to! Sheet breaks from a paper manufacturing is recommended to readStep-by-step understanding LSTM Autoencoder for data! Sharing concepts, ideas and codes y = 0 a paper manufacturing the trick is to regularization. Improves the model produces poor accuracy the Encoder uses two LSTM layers to better understand and further improve the below. However, in the test the data preprocessing steps for LSTM model is a array! Training mechanism improves the model & # x27 ; s feature extraction and prediction capabilities for time Series data with. And the LSTM Encoder consists of 4 LSTM cells input data to an LSTM Autoencoder this! Decoder consists of 4 LSTM cells data and verify if the shifting is correct through the layers of an Autoencoder! A paper manufacturing multiple variables observed over a period of time dataset: Event!
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